Design Optimization of Real-Size Steel Frames Using Monitored Convergence Curve

dc.authorid Kazemzadeh Azad, Saeid/0000-0001-9309-607X
dc.authorscopusid 57193753354
dc.contributor.author Azad, Saeid Kazemzadeh
dc.contributor.author Azad, Saeıd Kazemzadeh
dc.contributor.author Azad, Saeıd Kazemzadeh
dc.contributor.other Department of Civil Engineering
dc.contributor.other Department of Civil Engineering
dc.date.accessioned 2024-07-05T15:39:30Z
dc.date.available 2024-07-05T15:39:30Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp [Azad, Saeid Kazemzadeh] Atilim Univ, Dept Civil Engn, Ankara, Turkey en_US
dc.description Kazemzadeh Azad, Saeid/0000-0001-9309-607X en_US
dc.description.abstract It is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts. en_US
dc.identifier.citationcount 16
dc.identifier.doi 10.1007/s00158-020-02692-3
dc.identifier.endpage 288 en_US
dc.identifier.issn 1615-147X
dc.identifier.issn 1615-1488
dc.identifier.issue 1 en_US
dc.identifier.scopus 2-s2.0-85089293139
dc.identifier.scopusquality Q1
dc.identifier.startpage 267 en_US
dc.identifier.uri https://doi.org/10.1007/s00158-020-02692-3
dc.identifier.uri https://hdl.handle.net/20.500.14411/3232
dc.identifier.volume 63 en_US
dc.identifier.wos WOS:000558632700001
dc.identifier.wosquality Q1
dc.institutionauthor Azad, Saeıd Kazemzadeh
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 18
dc.subject Structural optimization en_US
dc.subject Monitored convergence curve en_US
dc.subject Steel frames en_US
dc.subject Discrete optimization en_US
dc.subject Metaheuristic techniques en_US
dc.subject Sizing optimization en_US
dc.title Design Optimization of Real-Size Steel Frames Using Monitored Convergence Curve en_US
dc.type Article en_US
dc.wos.citedbyCount 19
dspace.entity.type Publication
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